Conference Paper

Application of intelligent water drops algorithm to workflow scheduling in cloud environment

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  • National Institute of Technical Teachers Training and Research,Chandigarh,India
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... Many researchers have applied the IWD algorithm to workflow scheduling in the cloud. Kalra et al. [33] modified the probability function of the IWD algorithm with the focus on minimizing the makespan. The algorithm performs better in cases of large workflows. ...
... The workflow tasks are assigned to the cloud VMs level by level according to the best paths discovered. The algorithm fares better when scheduled on heterogeneous VMs in place of Choudhary et al. 2018 [28] Chaudhary et al. 2018 [29] Chaudhary et al. 2018 [30] Biswas et al. 2019 [31] Karamoozian et al. 2019 [32] Kalra et al. 2017 [33] Elsherbiny et al. 2018 [34] Kalra et al. 2019 [35] Adhikari et al. 2019 [36] Malik et al. 2016 [37] Chaudhary et al. 2017 [38] Li et al. 2017 [39] Moschakis et al. 2015 [40] Yuan et al. 2016 [41] Yuan et al. 2017 [42] Bukhsh et al. 2018 [43] Yuan et al. 2020 [44] Algorithm GSA IWD HS SA ...
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... The IWD algorithm is present in the solution of varied problems such as the following: classification of spam email in [15], workflow scheduling in a cloud environment [5], natural terrain feature identification [6], the capacitated vehicle routing problem [18], the multi-echelon supply chain optimization problem [7], multiobjective job shop scheduling [10], the optimal reactive power dispatch problem [8], and the robot path planning problem [12]. ...
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A unified view of metaheuristics. This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems. Designing efficient metaheuristics for multi-objective optimization problems. Designing hybrid, parallel, and distributed metaheuristics. Implementing metaheuristics on sequential and parallel machines. Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.
Intelligent water drops algorithm A new optimization method for solving the vehicle routing problem
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